In [1]:
from keras import layers
from keras import models
C:\Users\Nagendra\Anaconda3\lib\site-packages\h5py\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`. from ._conv import register_converters as _register_converters Using TensorFlow backend.
In [2]:
#Create Directories
train_dir="D:\\Nagendra\\MBA02\Datasets\\Driver_Distraction\\train\\"
test_dir="D:\\Nagendra\\MBA02\Datasets\\Driver_Distraction\\test\\"
validation_dir = "D:\\Nagendra\\MBA02\Datasets\\Driver_Distraction\\validation\\"
In [3]:
#Fetch images from directories using ImageDataGenerator
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale=1./255) #Rescales all images by 1/255
test_datagen = ImageDataGenerator(rescale=1./255) #Rescales all images by 1/255
train_generator = train_datagen.flow_from_directory (train_dir,target_size=(150, 150),batch_size=20,class_mode='categorical') #resizes all images to 150*150
validation_generator = test_datagen.flow_from_directory (validation_dir,target_size=(150, 150),batch_size=20, class_mode='categorical')
Found 20424 images belonging to 10 classes. Found 2000 images belonging to 10 classes.
In [4]:
#the network architecture
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu',input_shape=(150, 150, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
In [5]:
#compilation
from keras import optimizers
model.compile(loss='categorical_crossentropy',optimizer=optimizers.RMSprop(lr=1e-4),metrics=['accuracy'])
In [6]:
train_datagen = ImageDataGenerator(
rescale=1./255,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,)
In [7]:
test_datagen = ImageDataGenerator(rescale=1./255)#validation data should not be augmented
In [8]:
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(150, 150),
batch_size=32,
class_mode='categorical')
Found 20424 images belonging to 10 classes.
In [9]:
validation_generator = test_datagen.flow_from_directory(
validation_dir,
target_size=(150, 150),
batch_size=32,
class_mode='categorical')
Found 2000 images belonging to 10 classes.
In [14]:
#fit the model
history = model.fit_generator(
train_generator,
steps_per_epoch=100,
epochs=5,
validation_data=validation_generator,
validation_steps=50)
Epoch 1/5 100/100 [==============================] - 208s 2s/step - loss: 1.5979 - acc: 0.4313 - val_loss: 1.2103 - val_acc: 0.6062 Epoch 2/5 100/100 [==============================] - 210s 2s/step - loss: 1.5957 - acc: 0.4391 - val_loss: 1.0807 - val_acc: 0.6237 Epoch 3/5 100/100 [==============================] - 213s 2s/step - loss: 1.5803 - acc: 0.4353 - val_loss: 1.1781 - val_acc: 0.5915 Epoch 4/5 100/100 [==============================] - 214s 2s/step - loss: 1.6029 - acc: 0.4228 - val_loss: 1.2515 - val_acc: 0.5783 Epoch 5/5 100/100 [==============================] - 218s 2s/step - loss: 1.5172 - acc: 0.4709 - val_loss: 1.2732 - val_acc: 0.5656
In [16]:
import matplotlib.pyplot as plt
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(acc) + 1)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show();
In [ ]:
#Pre-trained model
In [10]:
import os
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
In [11]:
from keras.applications import VGG16
conv_base = VGG16(weights='imagenet',include_top=False,input_shape=(150, 150, 3))
In [12]:
conv_base.summary()
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_1 (InputLayer) (None, 150, 150, 3) 0 _________________________________________________________________ block1_conv1 (Conv2D) (None, 150, 150, 64) 1792 _________________________________________________________________ block1_conv2 (Conv2D) (None, 150, 150, 64) 36928 _________________________________________________________________ block1_pool (MaxPooling2D) (None, 75, 75, 64) 0 _________________________________________________________________ block2_conv1 (Conv2D) (None, 75, 75, 128) 73856 _________________________________________________________________ block2_conv2 (Conv2D) (None, 75, 75, 128) 147584 _________________________________________________________________ block2_pool (MaxPooling2D) (None, 37, 37, 128) 0 _________________________________________________________________ block3_conv1 (Conv2D) (None, 37, 37, 256) 295168 _________________________________________________________________ block3_conv2 (Conv2D) (None, 37, 37, 256) 590080 _________________________________________________________________ block3_conv3 (Conv2D) (None, 37, 37, 256) 590080 _________________________________________________________________ block3_pool (MaxPooling2D) (None, 18, 18, 256) 0 _________________________________________________________________ block4_conv1 (Conv2D) (None, 18, 18, 512) 1180160 _________________________________________________________________ block4_conv2 (Conv2D) (None, 18, 18, 512) 2359808 _________________________________________________________________ block4_conv3 (Conv2D) (None, 18, 18, 512) 2359808 _________________________________________________________________ block4_pool (MaxPooling2D) (None, 9, 9, 512) 0 _________________________________________________________________ block5_conv1 (Conv2D) (None, 9, 9, 512) 2359808 _________________________________________________________________ block5_conv2 (Conv2D) (None, 9, 9, 512) 2359808 _________________________________________________________________ block5_conv3 (Conv2D) (None, 9, 9, 512) 2359808 _________________________________________________________________ block5_pool (MaxPooling2D) (None, 4, 4, 512) 0 ================================================================= Total params: 14,714,688 Trainable params: 14,714,688 Non-trainable params: 0 _________________________________________________________________
In [13]:
datagen = ImageDataGenerator(rescale=1./255)
batch_size = 10
In [14]:
def extract_features(directory, sample_count):
features = np.zeros(shape=(sample_count, 4, 4, 512))
#print("features:")
#print(features)
labels = np.zeros(shape=(sample_count,10))
#print("labels:")
#print(labels)
generator = datagen.flow_from_directory(
directory,
target_size=(150, 150),
batch_size=batch_size,
class_mode='categorical')
i = 0
for inputs_batch, labels_batch in generator:
features_batch = conv_base.predict(inputs_batch)
print("features_batch")
print(features_batch)
features[i * batch_size : (i + 1) * batch_size] = features_batch
labels[i * batch_size : (i + 1) * batch_size] = labels_batch
i += 1
if i * batch_size >= sample_count:
break
return features, labels
In [ ]:
train_features, train_labels = extract_features(train_dir, 20424)
Found 20424 images belonging to 10 classes.
features_batch
[[[[5.3270859e-01 6.7546368e-03 3.1509072e-01 ... 0.0000000e+00
1.2274225e+00 0.0000000e+00]
[1.4557353e+00 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
1.4314539e+00 0.0000000e+00]
[1.1287713e+00 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
1.0355377e+00 0.0000000e+00]
[1.1621962e+00 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
1.4662082e+00 0.0000000e+00]]
[[1.4177979e+00 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
1.5154626e+00 0.0000000e+00]
[1.7524675e+00 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
1.3154030e+00 0.0000000e+00]
[1.7314585e+00 0.0000000e+00 7.3621273e-02 ... 0.0000000e+00
7.3417687e-01 0.0000000e+00]
[1.1125716e+00 0.0000000e+00 8.9829612e-01 ... 0.0000000e+00
2.2970289e-01 0.0000000e+00]]
[[1.6676819e+00 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
1.3899167e+00 0.0000000e+00]
[1.9819213e+00 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
8.1232667e-01 0.0000000e+00]
[2.6657698e+00 0.0000000e+00 1.2703551e+00 ... 0.0000000e+00
2.7259102e-01 0.0000000e+00]
[2.2224073e+00 0.0000000e+00 1.0349742e+00 ... 0.0000000e+00
6.5201437e-01 0.0000000e+00]]
[[1.7832530e+00 0.0000000e+00 2.2860375e-01 ... 0.0000000e+00
9.6638298e-01 0.0000000e+00]
[2.2069411e+00 0.0000000e+00 3.5681394e-01 ... 0.0000000e+00
7.3671138e-01 0.0000000e+00]
[2.8421001e+00 0.0000000e+00 1.4602280e+00 ... 4.5424309e-01
0.0000000e+00 0.0000000e+00]
[2.4865909e+00 0.0000000e+00 1.3137124e+00 ... 5.0465304e-01
6.1400402e-01 0.0000000e+00]]]
[[[6.7970097e-01 0.0000000e+00 3.9776683e-02 ... 0.0000000e+00
6.3068902e-01 0.0000000e+00]
[1.2061137e-01 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
1.1336336e+00 0.0000000e+00]
[4.6842852e-01 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
1.2240949e+00 0.0000000e+00]
[1.0845983e+00 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
1.4226689e+00 0.0000000e+00]]
[[1.1533252e+00 0.0000000e+00 2.1352589e-02 ... 0.0000000e+00
3.7758762e-01 0.0000000e+00]
[4.2030692e-01 0.0000000e+00 8.6586881e-01 ... 0.0000000e+00
3.0455890e-01 0.0000000e+00]
[0.0000000e+00 0.0000000e+00 9.9569440e-01 ... 0.0000000e+00
1.4923260e-01 0.0000000e+00]
[3.3808890e-01 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
0.0000000e+00 0.0000000e+00]]
[[1.1135857e+00 0.0000000e+00 1.6100235e+00 ... 0.0000000e+00
1.1168224e+00 0.0000000e+00]
[6.0599244e-01 0.0000000e+00 7.2016603e-01 ... 5.0157392e-01
8.7767410e-01 0.0000000e+00]
[0.0000000e+00 0.0000000e+00 1.0688365e+00 ... 6.9210756e-01
1.9287601e-01 0.0000000e+00]
[4.9831241e-01 0.0000000e+00 9.4907999e-02 ... 1.7658499e-01
0.0000000e+00 0.0000000e+00]]
[[1.7182150e+00 0.0000000e+00 1.7643416e+00 ... 5.5271491e-02
1.3130933e+00 0.0000000e+00]
[1.8808405e+00 0.0000000e+00 4.5180947e-01 ... 1.4311023e-01
1.1751306e+00 0.0000000e+00]
[1.1650643e+00 0.0000000e+00 5.2783853e-01 ... 1.6242638e-03
2.7444237e-01 0.0000000e+00]
[1.5665669e+00 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
7.2941720e-02 0.0000000e+00]]]
[[[8.9377165e-04 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
4.9490708e-01 0.0000000e+00]
[4.4864401e-01 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
1.1721110e+00 0.0000000e+00]
[1.0819314e+00 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
7.6160395e-01 0.0000000e+00]
[1.3710985e+00 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
2.3404148e-01 0.0000000e+00]]
[[1.1202689e+00 0.0000000e+00 2.8511429e-01 ... 0.0000000e+00
0.0000000e+00 0.0000000e+00]
[2.5924802e-01 0.0000000e+00 9.7359872e-01 ... 1.4113465e-01
9.2155093e-01 0.0000000e+00]
[8.5764432e-01 0.0000000e+00 9.8693401e-01 ... 0.0000000e+00
7.6811504e-01 0.0000000e+00]
[1.1592646e+00 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
3.2641822e-01 0.0000000e+00]]
[[1.8949082e+00 0.0000000e+00 2.0680302e-01 ... 0.0000000e+00
3.6904791e-01 0.0000000e+00]
[1.2932459e+00 0.0000000e+00 1.2387960e+00 ... 5.3513050e-01
7.8011549e-01 0.0000000e+00]
[0.0000000e+00 0.0000000e+00 1.3894382e+00 ... 5.7383132e-01
7.9050130e-01 0.0000000e+00]
[2.1610543e-02 0.0000000e+00 4.7881258e-01 ... 3.5021010e-01
6.3848716e-01 0.0000000e+00]]
[[2.1500962e+00 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
8.8174218e-01 0.0000000e+00]
[1.9087213e+00 0.0000000e+00 3.4377909e-01 ... 0.0000000e+00
7.3429495e-01 0.0000000e+00]
[3.0789047e-01 0.0000000e+00 6.7285460e-01 ... 0.0000000e+00
2.1491742e-01 0.0000000e+00]
[7.8919232e-01 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
4.2984065e-01 0.0000000e+00]]]
...
[[[2.0945437e+00 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
9.7980666e-01 0.0000000e+00]
[1.7913755e+00 0.0000000e+00 4.0920901e-01 ... 0.0000000e+00
1.7879051e-01 0.0000000e+00]
[6.0831261e-01 0.0000000e+00 4.1675162e-01 ... 0.0000000e+00
2.8463131e-01 0.0000000e+00]
[9.6211839e-01 0.0000000e+00 8.0125540e-02 ... 0.0000000e+00
3.3515418e-01 0.0000000e+00]]
[[2.5491233e+00 0.0000000e+00 1.9060931e-01 ... 0.0000000e+00
9.6214366e-01 0.0000000e+00]
[2.1187673e+00 0.0000000e+00 1.6337454e+00 ... 0.0000000e+00
2.0429069e-01 0.0000000e+00]
[6.3108873e-01 0.0000000e+00 1.2633212e+00 ... 0.0000000e+00
1.8131754e-01 1.4369304e-01]
[1.3369035e+00 0.0000000e+00 8.7131083e-01 ... 0.0000000e+00
3.9017180e-01 0.0000000e+00]]
[[2.8154047e+00 0.0000000e+00 3.6035258e-01 ... 0.0000000e+00
9.1260833e-01 0.0000000e+00]
[2.1478133e+00 0.0000000e+00 1.1155019e+00 ... 0.0000000e+00
2.7068526e-01 2.0073142e-02]
[3.1850174e-01 0.0000000e+00 1.1005169e+00 ... 0.0000000e+00
0.0000000e+00 5.5455081e-02]
[1.6301664e+00 0.0000000e+00 1.0945064e+00 ... 0.0000000e+00
0.0000000e+00 0.0000000e+00]]
[[2.7212865e+00 0.0000000e+00 2.2383690e-02 ... 0.0000000e+00
8.2539344e-01 0.0000000e+00]
[2.2130525e+00 0.0000000e+00 9.4803911e-01 ... 0.0000000e+00
6.0295045e-02 0.0000000e+00]
[1.1728151e+00 0.0000000e+00 9.4388264e-01 ... 0.0000000e+00
0.0000000e+00 0.0000000e+00]
[1.1266730e+00 0.0000000e+00 9.1012776e-01 ... 0.0000000e+00
0.0000000e+00 0.0000000e+00]]]
[[[2.1782100e+00 0.0000000e+00 6.6977262e-02 ... 0.0000000e+00
7.1058536e-01 0.0000000e+00]
[1.7693384e+00 0.0000000e+00 1.5702083e+00 ... 0.0000000e+00
1.0270125e-01 0.0000000e+00]
[3.0343115e-02 0.0000000e+00 1.1760010e+00 ... 0.0000000e+00
4.6040177e-01 0.0000000e+00]
[5.4400551e-01 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
9.3608785e-01 0.0000000e+00]]
[[3.0153444e+00 0.0000000e+00 5.0279075e-01 ... 0.0000000e+00
6.5921140e-01 0.0000000e+00]
[2.1966088e+00 0.0000000e+00 2.4717877e+00 ... 2.3553520e-01
2.6796651e-01 0.0000000e+00]
[0.0000000e+00 0.0000000e+00 2.0020518e+00 ... 2.7250949e-01
4.5651394e-01 0.0000000e+00]
[3.7753183e-01 1.0610956e-01 8.9925212e-01 ... 0.0000000e+00
4.8611289e-01 0.0000000e+00]]
[[2.9472976e+00 0.0000000e+00 4.6498165e-01 ... 0.0000000e+00
7.1326697e-01 0.0000000e+00]
[2.1881874e+00 0.0000000e+00 2.0557175e+00 ... 0.0000000e+00
8.0560857e-01 0.0000000e+00]
[9.5866293e-01 0.0000000e+00 1.6998456e+00 ... 3.6919570e-01
4.8082095e-01 0.0000000e+00]
[4.3042555e-02 0.0000000e+00 1.2534740e+00 ... 9.6113034e-02
2.3278412e-01 0.0000000e+00]]
[[3.2044318e+00 0.0000000e+00 2.4343801e-01 ... 0.0000000e+00
8.0516028e-01 0.0000000e+00]
[2.5479267e+00 0.0000000e+00 2.7387092e-01 ... 0.0000000e+00
1.3717524e+00 0.0000000e+00]
[1.6121842e+00 0.0000000e+00 1.3419507e+00 ... 0.0000000e+00
1.0284655e+00 0.0000000e+00]
[4.5748338e-01 0.0000000e+00 1.3631964e+00 ... 0.0000000e+00
4.8552412e-01 0.0000000e+00]]]
[[[2.5965815e+00 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
7.2707462e-01 0.0000000e+00]
[1.7281682e+00 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
8.4083110e-01 0.0000000e+00]
[7.9860884e-01 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
1.0369152e+00 0.0000000e+00]
[3.7333816e-01 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00
4.6485227e-01 0.0000000e+00]]
[[3.1864693e+00 0.0000000e+00 9.4809604e-01 ... 0.0000000e+00
7.9014707e-01 0.0000000e+00]
[1.9084917e+00 0.0000000e+00 1.3875237e+00 ... 6.1809230e-01
3.7384236e-01 0.0000000e+00]
[1.0026140e+00 0.0000000e+00 1.8967204e+00 ... 5.5658060e-01
7.6072979e-01 0.0000000e+00]
[8.5042417e-01 0.0000000e+00 1.3420496e+00 ... 0.0000000e+00
1.1555332e-01 0.0000000e+00]]
[[1.9057472e+00 0.0000000e+00 8.8748008e-01 ... 0.0000000e+00
9.7878766e-01 0.0000000e+00]
[1.1684229e+00 0.0000000e+00 2.3006916e+00 ... 6.9396001e-01
7.5429648e-01 0.0000000e+00]
[4.5749870e-01 0.0000000e+00 3.6321232e+00 ... 9.2675108e-01
7.5036627e-01 0.0000000e+00]
[1.1688412e+00 0.0000000e+00 2.0202751e+00 ... 2.9471734e-01
2.7694291e-01 8.9975350e-02]]
[[1.4799607e+00 0.0000000e+00 2.3861861e-01 ... 0.0000000e+00
1.0067334e+00 0.0000000e+00]
[1.0858011e+00 0.0000000e+00 2.1994820e+00 ... 5.2296251e-01
7.8439778e-01 0.0000000e+00]
[5.7621396e-01 0.0000000e+00 3.6013956e+00 ... 6.0877544e-01
5.2871990e-01 0.0000000e+00]
[7.7628350e-01 0.0000000e+00 1.7239878e+00 ... 0.0000000e+00
1.4574227e-01 0.0000000e+00]]]]
features_batch
[[[[2.165309 0. 0.13085642 ... 0. 0.650475
0. ]
[1.9496653 0. 2.2035167 ... 0. 0.5346451
0. ]
[0. 0. 1.5197002 ... 0. 0.48704898
0. ]
[0.06040378 0. 0. ... 0. 0.10977656
0. ]]
[[2.9664423 0. 0.7984158 ... 0. 0.64679176
0. ]
[2.3292572 0. 3.218352 ... 0. 0.6451752
0. ]
[0. 0.01939124 2.7649662 ... 0. 0.47681084
0.09145193]
[0. 0. 0.61695373 ... 0. 0.
0. ]]
[[2.1253095 0. 0.87789524 ... 0. 0.6895585
0. ]
[1.3743378 0. 2.3880858 ... 0. 1.2578033
0. ]
[0.49238545 0. 2.190253 ... 0. 0.8415904
0. ]
[0. 0. 0.51380664 ... 0. 0.4618262
0. ]]
[[2.4464805 0. 0.03762251 ... 0. 0.8003557
0. ]
[1.6634502 0. 0.04233804 ... 0. 1.493658
0. ]
[0.648799 0. 0.6556169 ... 0. 1.3765426
0. ]
[0.17295691 0. 0.5741935 ... 0. 1.054613
0. ]]]
[[[0.547428 0. 0. ... 0. 0.5661694
0. ]
[0.6929186 0. 0. ... 0. 1.2472758
0. ]
[0.6544813 0. 0. ... 0. 1.2581451
0. ]
[0.7893166 0. 0.29335004 ... 0. 1.5890487
0. ]]
[[1.1667941 0. 0. ... 0. 0.
0. ]
[0.5517838 0. 0.8069078 ... 0. 0.79546666
0. ]
[1.6569045 0. 0.82332385 ... 0. 0.37294266
0. ]
[1.375349 0. 0. ... 0. 0.28246203
0. ]]
[[1.9134845 0. 0.8446555 ... 0. 0.27353483
0. ]
[1.7153218 0. 1.0672752 ... 0.30520356 0.714023
0. ]
[2.329022 0. 1.533478 ... 0.3559484 0.
0. ]
[2.4455009 0. 0.69783 ... 0. 0.
0. ]]
[[2.0217915 0. 0.47998556 ... 0. 0.7446623
0. ]
[2.1914654 0. 0.03727967 ... 0. 0.7865427
0. ]
[2.2963529 0. 0.634708 ... 0. 0.01772821
0. ]
[2.4353862 0. 0.41849014 ... 0. 0.
0. ]]]
[[[0.74684274 0. 0.3780212 ... 0. 0.27305347
0. ]
[1.3441447 0. 0. ... 0. 0.6894494
0. ]
[1.5201746 0. 0. ... 0. 1.4872825
0. ]
[1.4004573 0. 0. ... 0. 2.043247
0. ]]
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[[[0.15572107 0. 0. ... 0. 0.7595751
0. ]
[0.88221467 0. 0.22940984 ... 0. 0.4113472
0. ]
[1.8401482 0. 0.46731535 ... 0. 0.45434535
0. ]
[1.8685721 0. 0.6390954 ... 0. 0.9363197
0. ]]
[[0. 0. 0.22843236 ... 0. 1.2558572
0. ]
[2.177834 0. 0.04754514 ... 0. 1.2090268
0. ]
[2.6420202 0. 0. ... 0. 0.
0. ]
[1.9151003 0. 0. ... 0. 0.
0. ]]
[[1.3749137 0. 0. ... 0. 1.2739345
0. ]
[2.135291 0. 0.85773253 ... 0.0687796 1.228915
0. ]
[2.5864873 0. 0.8734764 ... 0.18997306 0.22643909
0. ]
[1.7876892 0. 0. ... 0. 0.69474804
0. ]]
[[1.5367907 0. 0. ... 0. 0.79822344
0. ]
[1.5460728 0. 0.7451936 ... 0. 0.46949306
0. ]
[1.4271411 0. 0.83311236 ... 0.20838703 0.17931572
0. ]
[1.0442162 0. 0.06443709 ... 0. 0.58684695
0. ]]]
[[[0.09806344 0. 0.36594617 ... 0. 0.62017965
0. ]
[0.761995 0. 0.69137764 ... 0. 0.26129037
0. ]
[1.8987039 0. 0.20083147 ... 0. 0.47731018
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[1.9424987 0. 0.5674397 ... 0. 0.54490566
0. ]]
[[0.6876912 0. 0.69437724 ... 0. 0.410645
0. ]
[0.8104002 0. 1.1896794 ... 0. 0.36807686
0. ]
[1.9230925 0. 0.33093104 ... 0. 0.
0. ]
[2.2287118 0. 0.2570496 ... 0. 0.
0. ]]
[[0.8601688 0. 0.36477804 ... 0. 0.6868789
0. ]
[0.180138 0. 1.5423212 ... 0.02644729 0.2515248
0. ]
[1.3749976 0. 1.0450097 ... 0. 0.38212875
0. ]
[1.6685032 0. 0.20001459 ... 0.10737687 0.63536835
0. ]]
[[0.74944526 0. 0.5843116 ... 0. 0.6921679
0. ]
[0.48532748 0. 0.45459914 ... 0. 0.5227416
0. ]
[1.799344 0. 0.1440854 ... 0. 0.81869066
0. ]
[1.7293578 0. 0.15018216 ... 0. 0.7445851
0. ]]]]
In [ ]:
validation_features, validation_labels = extract_features(validation_dir, 2000)
In [ ]:
#test_features, test_labels = extract_features(test_dir, 79726)
In [ ]:
train_features = np.reshape(train_features, (20424, 4 * 4 * 512))
validation_features = np.reshape(validation_features, (2000, 4 * 4 * 512))
#test_features = np.reshape(test_features, (1000, 4 * 4 * 512))
In [ ]:
from keras import models
from keras import layers
from keras import optimizers
model = models.Sequential()
model.add(layers.Dense(256, activation='relu', input_dim=4 * 4 * 512))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(10, activation='softmax'))
model.compile(optimizer=optimizers.RMSprop(lr=2e-5),
loss='categorical_crossentropy',
metrics=['accuracy'])
In [ ]:
history = model.fit(train_features, train_labels,
epochs=30,
batch_size=20,
validation_data=(validation_features, validation_labels))
In [ ]:
import matplotlib.pyplot as plt
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(acc) + 1)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show();